1. randomly select a number of areas in Southampton
  2. find out buildings in selected areas with pv - using satellite images
  3. run model on these areas and see if these buildings were identified as suitable
  4. discuss if conflict is found

Then a number of cells are to be randomly selected, and eyeball check to find out buildings with pv on roof.

##  [1] "13"  "216" "651" "595" "232" "350" "589" "544" "252" "494"
## [1] "sf"         "data.frame"
## Simple feature collection with 6 features and 2 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.48 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##     layer  ID                       geometry
## 13    100  13 POLYGON ((-1.4 50.9, -1.39 ...
## 216   100 216 POLYGON ((-1.43 50.9, -1.42...
## 651   100 651 POLYGON ((-1.34 50.9, -1.34...
## 595   100 595 POLYGON ((-1.4 50.9, -1.4 5...
## 232   100 232 POLYGON ((-1.48 50.9, -1.47...
## 350   100 350 POLYGON ((-1.46 50.9, -1.46...

Identify buildings with PV

Patrick’s comments: 1) 3 of 10 sample randomly selected areas are industrial and have no PV at the moment 2) This result does not help the validation, and so no need to mention them 3) It is better to target residential areas and gain greater sample size 4) Commercial buildings have larger roofs with higher PV potential, but their feasibility needs more rigorous assessment, so can be excluded from the scope of this work 5) New built houses have PV regardless of their feasibility, and they can also be eliminated from sample.

Re-select sample areas from residential areas, and eyeball existing PV.

##   ID Seed
## 1 13   26
## 2 13   52
## 3 13   61
## 4 13   89
## Simple feature collection with 10 features and 2 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.45 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 10 x 3
##       ID Count                                                    geometry
##    <int> <int>                                               <POLYGON [°]>
##  1    51   133 ((-1.39 50.9, -1.38 50.9, -1.38 50.9, -1.39 50.9, -1.39 50…
##  2   651   163 ((-1.34 50.9, -1.34 50.9, -1.34 50.9, -1.34 50.9, -1.34 50…
##  3   527   142 ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
##  4   584   188 ((-1.36 50.9, -1.35 50.9, -1.35 50.9, -1.36 50.9, -1.36 50…
##  5   673   206 ((-1.4 50.9, -1.39 50.9, -1.39 50.9, -1.4 50.9, -1.4 50.9))
##  6   502   117 ((-1.35 50.9, -1.34 50.9, -1.34 50.9, -1.35 50.9, -1.35 50…
##  7   134   193 ((-1.45 50.9, -1.44 50.9, -1.44 50.9, -1.45 50.9, -1.45 50…
##  8    13    66 ((-1.4 50.9, -1.39 50.9, -1.39 50.9, -1.4 50.9, -1.4 50.9))
##  9   692    76 ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 10   173   138 ((-1.39 50.9, -1.39 50.9, -1.39 50.9, -1.39 50.9, -1.39 50…

The sample points are then exported to KML, and imported to google earth to eyeball existing pv

Google earth’s imagery date: 5/26/2017

## Simple feature collection with 6 features and 2 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.48 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
##     layer  ID                       geometry
## 13    100  13 POLYGON ((-1.4 50.9, -1.39 ...
## 216   100 216 POLYGON ((-1.43 50.9, -1.42...
## 651   100 651 POLYGON ((-1.34 50.9, -1.34...
## 595   100 595 POLYGON ((-1.4 50.9, -1.4 5...
## 232   100 232 POLYGON ((-1.48 50.9, -1.47...
## 350   100 350 POLYGON ((-1.46 50.9, -1.46...

All the selected buildings were eyeballed using Google earth, and the results are imported backed to R.

## Simple feature collection with 1680 features and 5 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.45 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
##    OBJECTID Id gridcode Shape_Leng Shape_Area
## 1         1  1        1          6          2
## 2         2  2        1          4          1
## 3         3  3        1          4          1
## 4         4  4        1          4          1
## 5         5  5        1          8          3
## 6         6  6        1          4          1
## 7         7  7        1          4          1
## 8         8  8        1          4          1
## 9         9  9        1          4          1
## 10       10 10        1         10          4
##                          geometry
## 1  POLYGON ((-1.39 50.9, -1.39...
## 2  POLYGON ((-1.39 50.9, -1.39...
## 3  POLYGON ((-1.39 50.9, -1.39...
## 4  POLYGON ((-1.39 50.9, -1.39...
## 5  POLYGON ((-1.39 50.9, -1.39...
## 6  POLYGON ((-1.39 50.9, -1.39...
## 7  POLYGON ((-1.39 50.9, -1.39...
## 8  POLYGON ((-1.39 50.9, -1.39...
## 9  POLYGON ((-1.39 50.9, -1.39...
## 10 POLYGON ((-1.39 50.9, -1.39...

Step 1: combine building polygons with points where PV are found using google satellite image.

Step 2: see the overlap of buildings-PV and GIS estimates.

## Simple feature collection with 6 features and 4 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.37 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 5
##     BID   NPV  NGIS   AGIS                                        geometry
##   <dbl> <int> <int>  <dbl>                                   <POLYGON [°]>
## 1  1423     1     2  2.00  ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 2  1632     1     1  1.000 ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 3  3984     2     5  9.00  ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.34 50…
## 4  3992     1     1  1.000 ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## 5  4068     1     1  2.00  ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## 6  4086     4    10 18.0   ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## Simple feature collection with 6 features and 4 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.37 ymin: 50.9 xmax: -1.34 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 5
##     BID   NPV  NGIS   AGIS                                        geometry
##   <dbl> <int> <int>  <dbl>                                   <POLYGON [°]>
## 1  1423     1     2  2.00  ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 2  1632     1     1  1.000 ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 3  3984     2     5  9.00  ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.34 50…
## 4  3992     1     1  1.000 ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## 5  4068     1     1  2.00  ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## 6  4086     4    10 18.0   ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## Simple feature collection with 6 features and 4 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -1.39 ymin: 50.9 xmax: -1.35 ymax: 50.9
## epsg (SRID):    4326
## proj4string:    +proj=longlat +datum=WGS84 +no_defs
## # A tibble: 6 x 5
##     BID   NPV  NGIS   AGIS                                        geometry
##   <dbl> <int> <int>  <dbl>                                   <POLYGON [°]>
## 1 11579     1     0 NA     ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 2 53874     1     0 NA     ((-1.39 50.9, -1.39 50.9, -1.39 50.9, -1.39 50…
## 3 53972     1     0 NA     ((-1.39 50.9, -1.39 50.9, -1.39 50.9, -1.39 50…
## 4  1632     1     1  1.000 ((-1.37 50.9, -1.37 50.9, -1.37 50.9, -1.37 50…
## 5  3992     1     1  1.000 ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…
## 6  4068     1     1  2.00  ((-1.35 50.9, -1.35 50.9, -1.35 50.9, -1.35 50…